{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e34d63c6",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bb388092",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "import json\n",
    "\n",
    "from transformers import MarianTokenizer, AutoTokenizer\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import collections"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39063baa",
   "metadata": {},
   "source": [
    "# Defining plot styles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d9965d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import pearsonr, kde, gaussian_kde\n",
    "import matplotlib\n",
    "from matplotlib import rc\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1d57619a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "plt.rcParams['font.family'] = 'serif'\n",
    "#plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['text.usetex'] = False\n",
    "# plt.rcParams['font.family'] = 'serif'\n",
    "# plt.rcParams['font.serif'] = ['Times New Roman']\n",
    "\n",
    "# plt.rcParams['font.monospace'] = 'Ubuntu Mono'\n",
    "plt.rcParams['font.size'] = 26\n",
    "plt.rcParams['axes.labelsize'] = 26\n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "plt.rcParams['axes.titlesize'] = 26\n",
    "plt.rcParams['axes.titleweight'] = 'bold'\n",
    "plt.rcParams['xtick.labelsize'] = 16\n",
    "plt.rcParams['ytick.labelsize'] = 16\n",
    "plt.rcParams['legend.fontsize'] = 18\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebef0624",
   "metadata": {},
   "source": [
    "# Load / Process data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a8fd0ad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer_de = AutoTokenizer.from_pretrained(\"facebook/mbart-large-50-many-to-many-mmt\")\n",
    "tokenizer_he = AutoTokenizer.from_pretrained(\"facebook/mbart-large-50-many-to-many-mmt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "94ffd1da",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_num_of_tokens_per_gender(professions, translations_dict, tokenizer, target_file):\n",
    "    male_count, male_tokens, female_count, female_tokens = 0, 0, 0, 0\n",
    "    male_num_of_tokens_map, female_num_of_tokens_map = {}, {}\n",
    "    for profession in professions:\n",
    "        m, f = list(translations_dict[profession]['Male']), list(translations_dict[profession]['Female'])\n",
    "\n",
    "\n",
    "        for mp in m:\n",
    "            male_count += 1\n",
    "            male_num_of_tokens = len(tokenizer(mp, add_special_tokens=False)['input_ids'])\n",
    "            male_tokens += male_num_of_tokens\n",
    "            if male_num_of_tokens not in male_num_of_tokens_map:\n",
    "                male_num_of_tokens_map[male_num_of_tokens] = 1\n",
    "            else:\n",
    "                male_num_of_tokens_map[male_num_of_tokens] += 1\n",
    "        for fp in f:\n",
    "            female_count += 1\n",
    "            female_num_of_tokens = len(tokenizer(fp, add_special_tokens=False)['input_ids'])\n",
    "            female_tokens += female_num_of_tokens\n",
    "            if female_num_of_tokens not in female_num_of_tokens_map:\n",
    "                female_num_of_tokens_map[female_num_of_tokens] = 1\n",
    "            else:\n",
    "                female_num_of_tokens_map[female_num_of_tokens] += 1\n",
    "    with open(target_file, 'w+') as f:\n",
    "        f.write(\"male: \" + str(male_tokens / male_count) + \"\\n\")\n",
    "        f.write(\"female: \" + str(female_tokens / female_count) + \"\\n\")\n",
    "    max_tokens = max(max(male_num_of_tokens_map.keys()),max(female_num_of_tokens_map.keys()))\n",
    "    return male_tokens / male_count, female_tokens / female_count, male_num_of_tokens_map, female_num_of_tokens_map, max_tokens\n",
    "\n",
    "def count_tokens(translations_dict, mp, count,tokenizer, tokens, num_of_tokens_map, gender):\n",
    "    professions = list(translations_dict[mp][gender])\n",
    "    count += len(professions)\n",
    "    for p in professions:\n",
    "        num_of_tokens = len(tokenizer(p, add_special_tokens=False)['input_ids'])\n",
    "        tokens += num_of_tokens\n",
    "        if num_of_tokens not in num_of_tokens_map:\n",
    "            num_of_tokens_map[num_of_tokens] = 1\n",
    "        else:\n",
    "            num_of_tokens_map[num_of_tokens] += 1\n",
    "    return tokens,count\n",
    "\n",
    "def get_annotations_dict(merged_annotations):\n",
    "    translations_dict = {}\n",
    "    professions = set()\n",
    "    with open(merged_annotations, 'r') as f:\n",
    "        lines = f.readlines()\n",
    "    for line in lines:\n",
    "        line = line.strip()\n",
    "        columns = line.split(\"\\t\")\n",
    "        english_profession = columns[0]\n",
    "        professions.add(english_profession)\n",
    "        if not english_profession in translations_dict:\n",
    "            translations_dict[english_profession] = {'Male': [], 'Female': []}\n",
    "        for i in range(1, len(columns)):\n",
    "            if columns[i] != \"\":\n",
    "                if i % 2 and columns[i]:\n",
    "                    translations_dict[english_profession]['Male'].append(columns[i])\n",
    "                else:\n",
    "                    translations_dict[english_profession]['Female'].append(columns[i])\n",
    "    return translations_dict, professions\n",
    "\n",
    "def get_num_of_tokens_per_stereotype(male_stereotype,female_stereotype,translations_dict, tokenizer, target_file):\n",
    "    stereotype_count, stereotype_tokens, anti_stereotype_count, anti_stereotype_tokens = 0, 0, 0, 0\n",
    "    stereotype_num_of_tokens_map, anti_stereotype_num_of_tokens_map = {}, {}\n",
    "\n",
    "    for mp in male_stereotype:\n",
    "        tokens,count = count_tokens(translations_dict, mp, stereotype_count,tokenizer, stereotype_tokens, stereotype_num_of_tokens_map,'Male')\n",
    "        stereotype_tokens+=tokens\n",
    "        stereotype_count+=count\n",
    "        tokens,count = count_tokens(translations_dict, mp, anti_stereotype_count,tokenizer, anti_stereotype_tokens, anti_stereotype_num_of_tokens_map,'Female')\n",
    "        anti_stereotype_tokens += tokens\n",
    "        anti_stereotype_count += count\n",
    "    for fp in female_stereotype:\n",
    "        tokens,count = count_tokens(translations_dict, fp, stereotype_count,tokenizer, stereotype_tokens, stereotype_num_of_tokens_map,'Female')\n",
    "        stereotype_tokens += tokens\n",
    "        stereotype_count += count\n",
    "        tokens,count = count_tokens(translations_dict, fp, anti_stereotype_count,tokenizer, anti_stereotype_tokens, anti_stereotype_num_of_tokens_map,'Male')\n",
    "        anti_stereotype_tokens += tokens\n",
    "        anti_stereotype_count += count\n",
    "    with open(target_file, 'w+') as f:\n",
    "        f.write(\"stereotype: \" + str(stereotype_tokens / stereotype_count) + \"\\n\")\n",
    "        f.write(\"anti stereotype: \" + str(anti_stereotype_tokens / anti_stereotype_count) + \"\\n\")\n",
    "    max_tokens = max(max(stereotype_num_of_tokens_map.keys()),max(anti_stereotype_num_of_tokens_map.keys()))\n",
    "    return stereotype_tokens / stereotype_count, anti_stereotype_tokens / anti_stereotype_count,\\\n",
    "           stereotype_num_of_tokens_map, anti_stereotype_num_of_tokens_map, max_tokens\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "619ed6cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "he_translations, professions = get_annotations_dict(\"../data/he_merged_translations_new.txt\")\n",
    "de_translations, professions = get_annotations_dict(\"../data/de_merged_translations_new.txt\")\n",
    "he_male_average_tokens, he_female_average_tokens, he_male_num_of_tokens_map, he_female_num_of_tokens_map, he_max_tokens = \\\n",
    "        get_num_of_tokens_per_gender(professions, he_translations, tokenizer_he, \"../data/he_tokens_per_gender_mbart50.txt\")\n",
    "de_male_average_tokens, de_female_average_tokens, de_male_num_of_tokens_map, de_female_num_of_tokens_map, de_max_tokens = \\\n",
    "    get_num_of_tokens_per_gender(professions, de_translations, tokenizer_de, \"../data/de_tokens_per_gender_mbart50.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "030df156",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"../data/male_stereotype\",\"r\") as f:\n",
    "    male_stereotype = f.readlines()\n",
    "    male_stereotype = [i.strip() for i in male_stereotype]\n",
    "with open(\"../data/female_stereotype\",\"r\") as f:\n",
    "    female_stereotype = f.readlines()\n",
    "    female_stereotype = [i.strip() for i in female_stereotype]\n",
    "\n",
    "\n",
    "he_stereotype_avg_tokens, he_anti_stereotype_avg_tokens, \\\n",
    "he_stereotype_num_of_tokens_map, he_anti_stereotype_num_of_tokens_map, he_max_tokens = \\\n",
    "    get_num_of_tokens_per_stereotype(male_stereotype, female_stereotype, he_translations, tokenizer_he, \"../data/he_tokens_per_stereotype_mbart50.txt\")\n",
    "\n",
    "de_stereotype_avg_tokens, de_anti_stereotype_avg_tokens, \\\n",
    "de_stereotype_num_of_tokens_map, de_anti_stereotype_num_of_tokens_map, de_max_tokens = \\\n",
    "    get_num_of_tokens_per_stereotype(male_stereotype, female_stereotype, de_translations, tokenizer_de, \"../data/de_tokens_per_stereotype_mbart50.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "39f88ea3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['construction worker',\n",
       " 'janitor',\n",
       " 'laborer',\n",
       " 'developer',\n",
       " 'supervisor',\n",
       " 'cook',\n",
       " 'manager',\n",
       " 'carpenter',\n",
       " 'sheriff',\n",
       " 'mover',\n",
       " 'guard',\n",
       " 'analyst',\n",
       " 'lawyer',\n",
       " 'mechanic',\n",
       " 'physician',\n",
       " 'CEO',\n",
       " 'driver',\n",
       " 'chief',\n",
       " 'farmer',\n",
       " 'salesperson']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_stereotype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6bd209bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['attendant',\n",
       " 'assistant',\n",
       " 'editor',\n",
       " 'secretary',\n",
       " 'auditor',\n",
       " 'clerk',\n",
       " 'baker',\n",
       " 'housekeeper',\n",
       " 'cashier',\n",
       " 'counselor',\n",
       " 'tailor',\n",
       " 'teacher',\n",
       " 'hairdresser',\n",
       " 'librarian',\n",
       " 'accountant',\n",
       " 'nurse',\n",
       " 'designer',\n",
       " 'writer',\n",
       " 'cleaner',\n",
       " 'receptionist']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_stereotype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6a29deae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({3: 14, 4: 6, 2: 25, 1: 5, 5: 3}, {4: 9, 5: 1, 3: 15, 2: 20, 1: 8})"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "he_stereotype_num_of_tokens_map, he_anti_stereotype_num_of_tokens_map"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad7f253d",
   "metadata": {},
   "source": [
    "# Create Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "17826b63",
   "metadata": {},
   "outputs": [],
   "source": [
    "def graphs_3_and_4(group1_num_of_tokens_map, group2_num_of_tokens_map, max_tokens, title, group1_name,group2_name):\n",
    "    for i in range(1,max_tokens+1):\n",
    "        if i not in group1_num_of_tokens_map:\n",
    "            group1_num_of_tokens_map[i] = 0\n",
    "        if i not in group2_num_of_tokens_map:\n",
    "            group2_num_of_tokens_map[i] = 0\n",
    "    group1_num_of_tokens_map = collections.OrderedDict(sorted(group1_num_of_tokens_map.items()))\n",
    "    group2_num_of_tokens_map = collections.OrderedDict(sorted(group2_num_of_tokens_map.items()))\n",
    "\n",
    "\n",
    "    \n",
    "    if 'Gender' in title:\n",
    "        graph_num = '3'\n",
    "        data_1 = pd.DataFrame({'tokens' : group1_num_of_tokens_map.keys(),\n",
    "                              'count': group1_num_of_tokens_map.values(),\n",
    "                              'gender': ['male'] * len(group1_num_of_tokens_map)})\n",
    "        data_2 = pd.DataFrame({'tokens' : group2_num_of_tokens_map.keys(),\n",
    "                              'count': group2_num_of_tokens_map.values(),\n",
    "                              'gender': ['female'] * len(group2_num_of_tokens_map)})\n",
    "\n",
    "        data= pd.concat([data_1, data_2])\n",
    "                                  \n",
    "        g = sns.catplot(x=\"tokens\", y=\"count\",\n",
    "                data=data, kind=\"bar\", hue='gender', \n",
    "                hue_order = ['male', 'female'],\n",
    "                palette=sns.color_palette(['lightblue', 'brown']),\n",
    "                height=4.5, aspect=1.8, legend=False)\n",
    "        leg_title = 'gender'\n",
    "        fig = g.fig\n",
    "        axes = g.axes\n",
    "        \n",
    "    else:\n",
    "        graph_num = '4'\n",
    "        data_1 = pd.DataFrame({'tokens' : group1_num_of_tokens_map.keys(),\n",
    "                              'count': group1_num_of_tokens_map.values(),\n",
    "                              'stereotype': ['pro'] * len(group1_num_of_tokens_map)})\n",
    "        data_2 = pd.DataFrame({'tokens' : group2_num_of_tokens_map.keys(),\n",
    "                              'count': group2_num_of_tokens_map.values(),\n",
    "                              'stereotype': ['anti'] * len(group2_num_of_tokens_map)})\n",
    "\n",
    "        data = pd.concat([data_1, data_2])\n",
    "          \n",
    "        g = sns.catplot(x=\"tokens\", y='count',\n",
    "                data=data, kind=\"bar\", hue='stereotype', \n",
    "                hue_order = ['pro', 'anti'],\n",
    "                palette=sns.color_palette(['mediumaquamarine', 'darkorange']),\n",
    "                height=4.5, aspect=1.8, legend=False)\n",
    "        leg_title = 'stereotype'\n",
    "        fig = g.fig\n",
    "        axes = g.axes\n",
    "\n",
    "        \n",
    "    axes[0,0].set_xlim(left=-0.5, right=3.5)\n",
    "    axes[0,0].set_ylim(bottom=0.0, top=40)\n",
    "        \n",
    "    if 'Hebrew' in title:\n",
    "        lang = 'he'\n",
    "        axes[0,0].legend(ncol=1,loc='lower right', bbox_to_anchor=(.99, 0.4), title=leg_title)\n",
    "    else:\n",
    "        lang = 'de'\n",
    "        \n",
    "    plt.tight_layout()\n",
    "\n",
    "    plt.savefig(f'../graphs/graph_{graph_num}_{lang}_mbart.pdf', dpi=300)\n",
    "    \n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "152e1262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 810x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphs_3_and_4(de_male_num_of_tokens_map, de_female_num_of_tokens_map, de_max_tokens, \n",
    "               \"German num of tokens per Gender\", \"Male\", \"Female\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "acf3ed0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1bNlSFy9edMC7AQAA9nD79I/k5OQC7ZeSkpJn++3TOAo6riMV12chOefn4YxccgpNeHi4lixZYn4dFBSk1q1bKz4+XkuXLtWkSZP0008/mdsTEhL01FNPyTAM/fDDD+aLNIKDg+Xp6amBAwfqz3/+szZu3FjSbwUAANhBUFCQPDw8lJaWJkk6e/Zsgfa7fbrJ7erWrauKFSua58EXZfpMSWvYsGGu1/b6LCTLi35Pnz6da8Ud2IfLnYGfOnWqzfVUW7VqJcnywpEPP/xQcXFx6tu3r8UV1s8884xq1qypn376yeKqbQAA4Bw8PT1zLRv5+++/F2i/I0eO5Nnu7u6ujh07ml//97//VVZWVoHGXrdunQYNGqRBgwbp8uXLBdrHHjp06JDrdUE+i9TUVJ06dSrffrev+Z7XWvS3GzdunAYNGqR33nmnwPuUVS4X4Hv06GFz/dXdu3dLkrp3755r+3/+8x9JUlhYmMU+5cqVU9euXSVJX375pT1LBQAAJWjAgAHm53/88Yf27duXZ/+srKwC/fV9+PDh5udxcXHaunVrgeqZOXOmPv/8c/3yyy+qVq1agfaxh8aNG+f6Zeb777/Pd59NmzYpIyMj336BgYG51pX/+uuvCzTH/vTp05oxY4Y+//zzfPvCBQP87dLT0xUVFaXRo0dr+fLl6tevn95++21z+/Xr13Xo0CFJshn8s7fv3bu3+AsGAADFYvDgwQoICDC/znk3Ums+//xzxcbG5jtuv379cgXiSZMm5RtaN23apM2bN0uSxowZk+8x7G3s2LHm5wcPHsx18yVrZs2aVeCxJ0+ebL7pU0xMjBYtWpTvPhMmTFBWVpa8vb31l7/8pcDHKqtcOsD3799fnp6eaty4sb799lutWbNGX331lby8vMx9oqOjzX/qqlGjhtVxAgMDJUknT54s/qIBAECxqFy5shYsWGB+vXLlSs2fP99q399++00vv/yyxXxxa8qVK6f//Oc/5otDt23bppEjR+Zadz6nffv2qX///pJu3S102LBhhX0rd+zpp5/OdZfU4cOH28w5U6ZM0a+//lqgz0K6NWU55y9Hf/3rX23+JcMwDL311ltatmyZpFt/lfD39y/o2yizTEZxrh3kYLGxsUpISNCZM2f02WefadmyZXrqqae0cOFCValSRdKt9Umzb8N79uzZXHcCy/bFF1+of//+8vDwUGpqqs3jpaam5mpPSkpSnTp1lJiYKB8fH/u+OQAAXERKSoqio6MVHBxcoLXG79QHH3ygl19+2fy6T58+euaZZ1S7dm3Fx8dr48aN+vjjj9WxY0f179/fPEUmKChIMTExNsc9ePCgevfubb6QNSQkREOGDFGzZs1UsWJFnTt3TuvWrdOKFSuUkZGh1q1b64cffrCYPvPHH3/oueeek3RrPfScMwAefvhhSVL16tW1dOlSSdLGjRv1/vvvS5JOnDhhDuJ+fn5q27atpFvTh28/05+cnKzw8HBt27ZN0q1fcF588UV16tRJFStW1KlTp7R06VL9/PPP+s9//qPZs2ebpwfVr19fDRo0sDm2JM2ePVuvvvqqMjMzZTKZ9Pjjj6tv376qW7euMjMzdejQIS1dulSRkZGSpIkTJ2rSpEkW43z22Wf67LPPJEkHDhzQhQsXJEk1a9bMdUfdMWPGWEyTLm4l/d01M8qQV155xZBktG7d2sjIyDAMwzB27txpSDIkGWfPnrW634oVKwxJhoeHR57jT5w40TxWzkdiYqLd3wsAAK7i5s2bxuHDh42bN2+W2DE3btxo3HvvvVZ/bnt7ext///vfjbS0NGPx4sXm7fXr18933ISEBGP8+PGGn5+f1bElGTVr1jSmTZtmpKamWh0jOjra5r7Zj6CgIHP/nDXaejz33HNWj5WSkmJMmjTJ8Pb2trpfkyZNjJ07dxqGYRidO3cu1NiGYRiRkZFG7969DTc3N5u1hYaGGps3b7Y5hq18dftj8eLF+f3z2J0jvruGYRgufQb+dteuXZO/v7/S0tL09ddf67HHHtPBgwfNv71FRUVZ/fPQokWLNGzYMFWrVs1iBZucOAMPAEDhOewspm5Nldm3b5/i4uLk6+uroKAgde3a1Tzdds6cORo1apQkqU2bNvrll18KNG5mZqb27Nmjw4cPKy4uTiaTSf7+/mrWrJlatGghNze3YntPRXHz5k1t3rxZJ0+e1M2bN1WjRg01a9ZMzZs3t8v4V69e1bZt23Tu3DklJCSoYsWKqlOnjtq1a6c6derY5RiO4KjvrkuuA29L5cqVVa9ePUVFRWnXrl167LHHFBwcLJPJJMMwFBsbazXAZ9/EqX79+nmO7+npKU9Pz2KpHQAA2F+LFi3UokULm+3nz583P69Xr16Bx3Vzc1OHDh0slmwsrby8vHKtHmNvfn5+uebc48641EWsFy5c0IIFC5SZmWmzT6VKlSTJfCOHSpUqqUmTJpKko0ePWt0ne3ubNm3sWS4AAChhv/32m2bPnq2oqKgC9f/555/Nz7OXlQYczaUCfFRUlF544QWbV1FnLykpyXzhhSQ99dRTkm4t6XS7rKws8zJP2f0AAIBz2rp1q0aPHm3zpo85/f7779q+fbskqUqVKuaVYwBHc6kAny3nElE5zZw5U9evX1elSpX0xBNPmLePGjVKd911l9auXavo6Ohc+3zxxRe6cOGCunbtmutOawAAwHktXbpU69evt9l+7tw5PfPMM+b13GfMmCFfX9+SKg/Ik0vNgc++acDs2bMVHx+vIUOGqG7dujp//ryWL1+uBQsWyNPTU0uWLMm15rufn59Wrlyp8PBw9ezZUx988IEaNmyoiIgI/fWvf1W9evXM65MCAADnl5aWpj59+qh3797q06ePatWqJS8vL8XGxmrr1q369NNPlZycLJPJpIkTJ+r55593dMmAmcutQhMZGakVK1Zo27ZtOn78uJKSkuTp6ang4GB17dpVo0aNsnkjgqNHj2rKlCnavHmzrl69qlq1aunxxx/XhAkTzOvGF0ZSUpJ8fX1ZhQYAgDyU5EoeZ8+e1bx587R69ep858F36dJF//jHP/TQQw8Va01wXo5ahcblAnxpQoAHACB/jgpBFy9e1IEDBxQdHa2EhARlZGTI19dXtWvXVocOHRQQEFBitcA5sYwkAABACQoMDFRgYKCjywAKzSUvYgUAAABcFQEeAAAAcCIEeAAAAMCJEOABAAAAJ0KABwAAAJwIAR4AAABwIgR4AAAAwImUigB/48YNJScnO7oMAAAAoNQrFQH+o48+kq+vrxo0aKDZs2c7uhwAAACg1LJ7gO/atat69uxZ6P0Mw9CpU6c0ZswYjR8/3t5lAQAAAC7B7gE+IiJCW7duLdQ+3bp109SpU9WmTRsZhqFZs2bp+vXr9i4NAAAAcHrlHV2AJDVp0kRNmjTR66+/rgcffFC7d+/Wzp071aNHD0eXBgAASpHvD8U4ugS7CG9Sz9EllIjBgwdr6dKl5tedO3dWRESE4wpyEaViDnw2k8mksLAwSdKJEyccXA0AAADuxDvvvKMDBw7ohRdecHQpLqVUnIHP6ddff5UkJSUlObgSAAAA3IlatWqpVq1aCggIcHQpLqXIAX7o0KE229LT0/Nsv11WVpauXbumw4cP69ixYzKZTDIMo6ilAQAAAC6ryAF+yZIlMplMVtuysrJyzXcqqJyhvVKlSkUtDQAAAHBZdzwH3jCMXA9r2wr6yPkLQZ06de60NAAAgDIhJiZGJpMp1yMiIkKRkZHq06eP/Pz8VKVKFXXp0kWbN28277dv3z716tVLfn5+8vb2VteuXbVr1y6L8Q3D0ObNmzVq1Ci1bNlSPj4+cnd3V/Xq1RUeHq41a9bc8Xu4evWqJk6cqGbNmsnb21sVK1ZUgwYNNHToUO3fv/+Ox3cldxzgb/+yWNtW0EdOLVu2vNPSAAAAyoRatWrpwIEDOnDggHnb9u3bNWrUKA0fPlzr16/Xm2++qV27dunhhx/Wjz/+qO3bt2vKlCl6+eWX9d133+nFF19URESEunbtqoMHD+Ya//Tp0woLC9OCBQsUFhamNWvWaPv27Zo+fbpiYmL0+OOPa+TIkUWuf//+/WratKnefvtthYWF6euvv9a6des0fPhwrVq1Sq1atdK8efOKPL6rKXUXsZpMJnXo0EFBQUGOLgUAAMApuLu7q2nTprm2/fvf/9ahQ4fk6+srSWrXrp1u3rypiRMnaty4capXr56++uorubu7S5JCQ0MVGxurZcuW6d1339Wnn35qcZwZM2bolVdeMb9u3769nnrqKbVs2VLz5s3TI488oj59+hSq9itXrig8PFwXLlzQwoULNWLECHNb586dFRYWpnbt2mnUqFFq1qyZQkNDCzW+K7qjM/DWpsHY2l7QR3BwsBYtWmSXNwcAAFBWDRo0yBzes3Xr1k2SFBkZqe7du5vDe7bse/Bs27Yt1/YqVapo4sSJGjZsmMVxvL29NXDgQEnSsmXLCl3nzJkzdf78eTVo0EDDhw+3aG/Tpo26deumrKwszZgxo9Dju6Iin4F/7rnnrG5funSp3NzcNGjQoAKP5enpKX9/f7Vt21a9evVS+fKl7g8DAAAATqVVq1YW23Iu52itvUaNGpKkixcv5tpepUoVTZo0yeaxsmdOHDlypNB1rly5UpLUqVMnmwukhISEaOPGjYqIiFBWVpbKlStVtzIqcUVOyosXL7a6fenSpXJ3d7fZDgAAgOJ31113WWzLeZI0r/a0tDSLtkuXLumDDz7Qhg0bdOLECSUnJysrK0vS/1YSTE5OLlSNycnJOnXqlKRb2dLWKobZx7l27ZquXr1qtfayhFPdAAAALii/s9SFOYudPeUmPj5eYWFhWrhwoYKDg+Xp6SlJWrt2rcaPH1/o+/gkJiaanz///PN66aWX8t3Hx8enUMdwRXYP8IsXL5abm5u9hwUAAICDDBs2TPHx8QoNDdWGDRsswv++ffuKNG7OOfoVKlSwuBAX1tk9wNuaGw8AAADnEx8fr//+97+SpL59+9p1/rm3t7fuvvtunTp1SkePHs2z7+eff67y5cvr6aefttvxnVWpuwIgJCRE9evXd3QZAAAA0P/mn0uyOUUmJiamyONnB/Jt27YpKSnJap8DBw5o0KBB+vbbb4t8HFdS6ubAnz592uqFEwAc7/tDMY4uweHCm9RzdAkAUKL8/f0VEhKio0eP6ssvv9Qrr7yS62LY5ORkq2vGF9Srr76qZcuW6dy5c3rzzTf14Ycf5mrPyMjQyy+/LHd3d40dO7bIx3ElJRLgk5OTlZiYqMzMzHz7FvbiBwAAAMji7qnR0dHy9/dXcHCwPDw8dOzYMV24cMHcHhUVpeTkZDVt2lTXr19XdHS0oqOjLcZr2rSp5syZo169eikyMlJdunTR6NGjVadOHUVFRWnq1Km6cuWKJCk9PV0HDx6Uh4eHGjVqpPPnz+vq1au6dOmSJOn69eu52iWpatWq+uGHHxQeHq45c+YoNjZWQ4cOVbVq1XTixAnNmjVLkZGRWrhwoe6///5i/QydhckopsS8ceNGLViwQNu3bzf/oxaEYRgymUwFCvulXVJSknx9fZWYmMgV03AJnIHnDDxQHFJSUhQdHa3g4GBVqFAhz76u8v+h4vh/ia011Lds2aJ69eopODjYarthGIqIiFCXLl1stkvS/v37NW3aNEVERCguLk6VKlVSkyZNNHDgQHl6eua6g2pQUJBiYmI0ePBgq0tDZrfnlJSUpA8//FCrV6/W8ePHlZKSoho1aqhz58565ZVX1KJFi4J8DCWqMN9de7J7gDcMQ8OHD9eSJUvMrwtdFAEeKJVc5QfnnSDAA/bnqBAE3ClHfXftPoVm0qRJuW7iZOu3QVuYQgMAAADYZtcAn5SUpPfff98itBc0lBc27AMAAABljV0D/NatW3Xjxg2ZTCbzXHaJYA4AAADYi10D/KlTp8zPs0M8AAAAAPuxa4BPSUnJ9frpp5/WwIEDdffdd6tixYr5nok3DEMhISFKT0+3Z1kAAACAy7BrgA8ICDA/79Gjh1asWGHP4QEAAIAyz64BvnHjxubn3bt3L9IYH330Ua5b9gIAAAD4H7sG+AceeECBgYGKjY0tcgh/9tln7VkSAAAA4FLK2XMwk8mkGTNmyDAMrV27tkhjhISEqH79+vYsCwAAAHAZdg3wkjRo0CC9+uqr2rlzp0aOHKnk5ORC7X/69GmLW+sCAAAAuMXud2KdMmWKvL29FRISogULFujTTz9VaGioGjduLF9fX5Uvn/chMzIy7F0SAAAA4DLsHuAnTZpkXi7SMAxdv35dGzdu1MaNGwu0f84bQAEAAADIze4BPlvOIM4NnQAAAAD7KLYAn30nVpPJVKgz6oR9AAAAwDa7X8SaE1NhAAAAAPsq1ik0AAAAAOyrWAK8yWRSp06dirTvtm3b7FwNAABA2fLNN9/ogw8+UGRkpJKTk+Xv7697771XAwcO1JAhQxxdnt0MHjxYS5cuNb/u3LmzIiIiHFdQCSmWAO/h4aEtW7YUaV8vLy+lpaXZuSIAAOAK5nfo4OgS7OKFn38utrGXLVumP//5z6pZs6bmzJmjRo0a6fjx4xo1apQyMjJcKsC/8847evXVVzVv3jzNnz/f0eWUmGKbQlNUTL0BAAAoumnTpkmSxo0bpwEDBkiSWrdurbS0NJ0+fdqRpdldrVq1VKtWLQUEBDi6lBJl9wC/ePFiubm5FXn/jz/+WJmZmXasCAAAoOw4fvy4JKlhw4a5tj/33HOOKAfFwO4B/k6/HIMGDbJTJQAAAGVP9l3tPTw8HFwJikuxLiNZFGfPntWZM2ccXQYAAIDTiImJsbj3TpcuXczb6tWrl6v/vn37NHDgQNWpU0eenp7y8/NTu3btNHXqVCUmJtocO/sRERGhyMhI9enTR35+fqpSpYq6dOmizZs35zpGr1695OfnJ29vb3Xt2lW7du2yWr9hGNq8ebNGjRqlli1bysfHR+7u7qpevbrCw8O1Zs2aO/6Mrl69qokTJ6pZs2by9vZWxYoV1aBBAw0dOlT79++/4/FLUqkL8I0aNdLdd9/t6DIAAACcRq1atXTgwAEdOHDAvG3RokXmbRs2bDBvf/fdd9W2bVtt3bpVEyZM0NatW7VkyRLdfffdevPNN9W8eXMdO3Ysz7G3b9+uUaNGafjw4Vq/fr3efPNN7dq1Sw8//LB+/PFHbd++XVOmTNHLL7+s7777Ti+++KIiIiLUtWtXHTx40KL+06dPKywsTAsWLFBYWJjWrFmj7du3a/r06YqJidHjjz+ukSNHFvnz2b9/v5o2baq3335bYWFh+vrrr7Vu3ToNHz5cq1atUqtWrTRv3rwij1/SSt1FrBIXsgIAABSGu7u7mjZtmmtbcHCwxbYvv/xSr7/+uvz8/PTrr7+qevXq5ra+ffvK399fc+bMUb9+/fT777/Lzc3N6tj//ve/dejQIfn6+kqS2rVrp5s3b2rixIkaN26c6tWrp6+++kru7u6SpNDQUMXGxmrZsmV699139emnn1p9HzNmzNArr7xift2+fXs99dRTatmypebNm6dHHnlEffr0KdRnc+XKFYWHh+vChQtauHChRowYYW7r3LmzwsLC1K5dO40aNUrNmjVTaGhoocZ3BLsH+ClTptzR/tnztgAAAGA/GRkZeu211yRJo0ePzhXes73xxhuaM2eODh8+rLVr16pfv35Wxxo0aJA5vGfr1q2bJk6cqMjISI0YMcIc3rP16NFDy5Yts3rPnypVqmjixIkaNmyYRZu3t7cGDhyoSZMmadmyZYUO8DNnztT58+fVoEEDDR8+3KK9TZs26tatmzZu3KgZM2bom2++KdT4jmD3AD9p0qRc868KyzCMO9r/zJkzWrp0qdavX6/Dhw8rOTlZVatWVatWrTR06FA9+eSTNvfds2ePpk2bpp07dyo5OVl33323+vfvr9dee02enp5FrgkAAMDRdu3apbNnz0qSHnroIat9atasKR8fHyUlJWnTpk02A3yrVq0stuVcytFae40aNSRJFy9etGirUqWKJk2aZLP2oKAgSdKRI0ds9rFl5cqVkqROnTrZzJghISHauHGjIiIilJWVpXLlSt0s81yKrTrDMIr0uBNbt25V/fr19c4776hr167auHGjjhw5orlz5+rEiRN66qmnNGDAAGVlZVnsu3z5coWGhuqPP/7Q6tWrdfDgQQ0dOlSTJ09Wx44dlZycfEe1AQAAOFLOCzW7dOmi8uXLW30kJSVJUp6Litx1110W28qXL1+gdls37Lx06ZLGjx+vtm3bqmrVqvLw8DDXlH1mvrB5LDk5WadOnZJ0a6lzW+95zpw5kqRr167p6tWrhTqGIxTbHPiinkW/kxB/9epVZWRkaNasWRo1apR5e4MGDdShQweFhIRoxYoV6tKlS675TydPntTQoUN11113ad26dapSpYokacyYMUpNTdWbb76p0aNH66OPPipybQAAAI6Uc3WZb7/9VnXq1Mmzv7e3t822/M5QF/YMdmRkpLp37674+HiFhYVp4cKFCg4ONs+AWLt2rcaPH1/onJjzPT///PN66aWX8t3Hx8enUMdwhFJxEWv2P8adTJ3J5ubmZnUt+po1a6pPnz5avny5Vq5cmSvAT506VampqRoyZIg5vGcbOXKkpkyZokWLFmnChAmqW7fuHdcIAABQ0nLOWQ8MDLS4MNWRhg0bpvj4eIWGhmrDhg0WvwDs27evSOPmfM8VKlQoVe/5Tjh0Ck227DVF73QKTXh4uOLj423+5lS7dm1JUnx8vHlbZmamVq9eLUkKCwuz2MfX11dt2rRRVlaWVq1adUf1AQAAOEqzZs3Mz48ePWqz39mzZ/Xxxx8rMjKyJMpSfHy8/vvf/0q6tRKOPeefe3t7m5cnz+s9S9Lnn3+uL7/80m7HLk7FcgbeZDKpU6dOefZJT09XYmKioqOjdePGDZlMJtWpU+eO1oB3d3e3uOI5p+yLJu677z7ztqioKCUkJEi6dQGDNSEhIdqxY4f27t1b5NoAAAAc6YEHHlDdunV15swZfffdd3rmmWes9ps9e7ZmzZqljRs3lkhdOa9NtHUyNyYmpsjjP/3005o2bZq2bdumpKQkqyd6Dxw4oEGDBmngwIF6+umni3ysklIsZ+A9PDy0ZcuWPB87duzQgQMHlJiYqJ9++kmtW7fWlStXNGrUKG3ZssXuNWVkZJi/iDlvBHDy5Enz8+yro28XGBho0RcAAMCZlC9fXu+9956kW+vB79mzx6LPgQMH9O9//1vt2rVTt27dSqQuf39/80nUL7/80mJJ8eTkZJvrxhfEq6++qtq1a+vmzZt68803LdozMjL08ssvy93dXWPHji3ycUqSw+fAu7m5qWvXrtq6davuv/9+PfPMM9qxY4fatm1r1+MsWrRIsbGxeuGFF9SmTRvz9uwrrbNvVGBNxYoVJcni1sK3S01NVWpqqsXYAAAAxSk9PT3X3VMlKTo6Wv7+/pKkxo0by93dXX/605907tw5vfbaa+revbtef/11de3aVRkZGdqxY4feffddBQQE6Isvvsg11u13T80eOzg4WB4eHjp27JguXLhgbo+KilJycrKaNm2q69evKzo6WtHR0RbjZc9JnzNnjnr16qXIyEh16dJFo0ePVp06dRQVFaWpU6fqypUr5vd58OBBeXh4qFGjRjp//ryuXr2qS5cuSZKuX7+eq12Sqlatqh9++EHh4eGaM2eOYmNjNXToUFWrVk0nTpzQrFmzFBkZqYULF+r++++/43+LkmAy7Hzb09TUVJlMJnl4eBR637ffflv/+Mc/dP/995vnQtnDiRMn1LJlSzVt2lSbN29WhQoVzG3Lly/XwIED5ebmZvMmUtOnT9cbb7yhRo0aWfzHkdOkSZM0efJki+2JiYlOcUUzkJ/vD8U4ugSHOzNigKNLKBVe+PlnR5cAF5KSkqLo6GgFBwfn+hltzfwOHUqoquJl7/+GYmJiFBwcbLM9Ojpa9erVM7/ev3+/Zs+erS1btujixYsqX768GjZsqMcff1yjR4+2yC22FhrZsmWL6tWrZ/PYhmEoIiJCXbp0sdmes6Zp06YpIiJCcXFxqlSpkpo0aaKBAwfK09Mz1wIkQUFBiomJ0eDBg7V06VKLcbPbc0pKStKHH36o1atX6/jx40pJSVGNGjXUuXNnvfLKK2rRooXVGvNSmO+uPdk9wN+JBQsW6MUXX5TJZNLBgwd1zz333PGYFy9eVGhoqKpUqaItW7ZY3DXsu+++M9/RKy0tzepZ+H/84x9666231KZNG/3yyy82j2XtDHydOnUI8HAZBHgCfDYCPOzJUSEIuFOO+u6WqttM/f777+bnRV0uKKcLFy4oLCxMAQEB2rRpk0V4l6T69eubn8fGxlodJ/vi15x9rfH09JSPj0+uBwAAAGBPpSLAx8fHa9GiRfr444/N27LnMhXV6dOn1alTJwUEBOinn36Sn5+f1X6NGjUyB3tbywtlb885dx4AAABwBLtfxFqYZSDT0tKUlJSk69evS8o9D+pOZvYcP35cYWFhuueee7RmzRp5eXmZ237//XeNHTtW69evl3Tr4tV+/fpp8eLF2rRpk7p3755rrMTERO3du1flypXTE088UeSaAAAAAHuwe4CPiYkp8k2Zcu5Xs2bNIh3/0KFD6tatm9q1a6cvv/zSfAvebPHx8frxxx9zbRs3bpw+//xzLV68WG+88UauqTZz585Vamqqhg4dqqCgoCLVBAAAANhLsS0jaetq5YLu27lz50Lvd/DgQXXp0kVxcXGKiYlRaGioRZ9r165ZbGvQoIE+/vhjDR48WD179tR7772nwMBAff3115o4caJatWql2bNnF+WtAAAAAHbl8HXgpVvTZbIDv8lk0hNPPKFatWoVepyffvpJcXFxkm4tRVQYzz77rBo2bKhp06apb9++Sk5OVnBwsCZMmKCxY8dyVTwAoNRxlSUVK1SvriZ//auuZGXJvVzhLs8LsMOKdYCzKRUBPju8G4ahkJAQzZ8/v0jj/O1vf9Pf/va3ItfRvn17rV27tsj7AwAAAMWt2FahMQyjUI8qVarotdde0969e1W1atXiKgsAAABwasVyBr58+fJauHBhvv3c3NxUuXJlBQcHq2nTpipXyD+bAQAAAGVNsQR4Nzc3Pffcc8UxNAAAcDWl56bwQKHcybLnd4JT3gAAwKEyU1JkZGYqiyAPJ5OZmSlJJT6LxO5n4K9evXpHS0gCAICyJT0pSTfj4pQaECBPNzdHlwMU2LVr1+Tu7i53d/cSPa7df13w9fWVj4+PvYcFAAAuLH7/fiVdu6b0rCxHlwIUyM2bN5WUlKTKlSuX+MnrEllGMjExURs3btSRI0d06dIlSVJAQIDuvfdede/encAPAEAZd3nXLlUKCpLuu08+lSvLs1w5lStAKEpJSSmB6oBbDMNQZmamrl27pqSkJHl6esrf37/E6yjWAH/27FmNHz9eK1asMM8Rup2bm5sGDRqkKVOmqHbt2sVZDgAAKKWy0tIU88UXun76tKo2ayYvf3+ZypWT8gnxSaxgBwdwd3dXlSpV5O/vLzcHTPsqtgC/ZcsW9evXT0lJSXleoZuRkaGlS5dqzZo1WrNmjTp16lRcJQEAgFIsKy1Nf2zdqj+2bpW7j4/cPD2lfAJ6/xUrSqg64JZy5crJ3d3dodd8FkuA37Nnjx555BGlp6dLUr5v0DAMJSQk6OGHH9aOHTvUqlWr4igLAAA4ifSkJKUXoF+FChWKvRagtLH7351u3LihAQMGKD09XSaTyRzebd2BVZK5X2pqqgYMGKCbN2/auywAAADAJdg9wC9ZskTR0dEWZ92zQ/rtj9udOHFCS5YssXdZAAAAgEuwe4CfN29ermBu68y7rTPxhmFo3rx59i4LAAAAcAl2nQN/6dIlHT582BzEK1asqAcffFBNmzZVjRo15OvrK09PT0lSamqqEhMTFRsbq4MHD2rHjh26ceOGJOnw4cO6fPmyqlWrZs/yAAAAAKdn1wC/Z88e8/ORI0dq+vTpqlSpUoH2vX79ul5//XXz2fc9e/aod+/e9iwPAAAAcHp2nUITGxsrSWrfvr0+/PDDAod3SapUqZLmzJmjdu3aSZIuXrxoz9IAAAAAl2DXAH/16lVJUlhYWJHH6N69e66xAAAAAPyPXQN8uf+72UL2+u9FkZaWlmssAAAAAP9j15Ts5+cnSfr222+VmZlZ6P0zMjL07bff5hoLAAAAwP/YNcDXrl1bknTkyBE9+eSTiomJKfC+0dHRevLJJ3XkyJFcYwEAAAD4H7uuQtOuXTvzGvDffPONvvnmGzVq1EhNmzZVYGCgfH195eHhIenWVJnExERdvHhRBw8eVFRUlHkck8mk9u3b27M0AAAAwCXYNcBXqVJFrVu31r59+8w3Zzp27FiucG5Ndl/pVnhv1aqVfH197VkaAAAA4BLsfqXoSy+9JMMwZDKZzI/87sKa3S/nGAAAAAAs2T3A9+/fX82bN8+1LWeYt/bI2a958+YaMGCAvcsCAAAAXILdA3z58uW1YsUK+fj45Npu6+x7znZfX1+tWLFCbm5u9i4LAAAAcAnFsth648aNtW3bNgUGBppDel5n3w3DUO3atbVt2zY1atSoOEoCAAAAXEKx3S3pvvvu05EjRzRu3DhVrVrV5hn4qlWravz48Tp06JCaNm1aXOUAAAAALsGuq9DcrnLlynr77bf11ltvae/evTp8+LAuX74sSapWrZqaNGmi1q1b55oHDwAAAMC2Yg3w2Uwmk9q2bau2bduWxOEAAAAAl1VsU2gK6tChQ0pOTnZ0GQAAAIBTsHuAP3/+vLp27Zrr0bt3b5v9p0+fLn9/f40YMUIXL160dzkAAACAS7H7FJo1a9YoIiIi1woz3t7eee6TlpamRYsW6ZtvvtG3337LVBsAAADABrufgf/hhx8k3Qru1apV02uvvaYvvvjCZv+OHTuqUaNGMgxDly9fVnh4OGfiAQAAABvsHuB37dolk8mk3r1768SJE5oxY4Z69epls//zzz+vo0ePasOGDapVq5bi4+M1YcIEe5cFAAAAuAS7BvgTJ04oISFB/v7++uKLL/KdOpNTt27d9NNPP6l8+fL68ssvdfPmTXuWBgAAALgEuwb4qKgoSVLfvn1VsWLFQu/fuHFj9e7dWzdu3NAvv/xiz9IAAAAAl2DXAH/mzBlJUt26dYs8xv333y9JOnr0qF1qAgAAAFyJXQP8tWvXJEnXr18v8hjp6emSpISEBHuUBAAAALgUuwb4jIwMSdKePXuKPEZkZKQkmZehBAAAAPA/dg3wfn5+kqRt27Zp69athd7/559/1oYNG3KNBQAAAOB/7Brgq1evLunWGvCPPvqo/v3vfyslJSXf/VJSUrRgwQL16tVLWVlZkqTAwEB7lgYAAAC4BLveibVNmzbm59euXdOLL76o0aNHq3Xr1mrYsKGqV6+uChUqSJJu3rypP/74Q8ePH9e+ffuUlpYmwzDM+7dr186epQEAAAAuwa4Bvnbt2qpfv75OnTolk8kkwzCUkpKinTt3aufOnTb3yw7u2fvce++9qlatmj1LAwAAAFyC3e/E+uKLL+YK5NmhPK9Hzn4mk0kvvPCCvcsCAAAAXILdA/yIESMs1oHPDui2Hjn73X333RoyZIi9ywIAAABcgt0DvLe3t7744gt5eHjk2m7r7HvOdk9PTy1fvlxeXl72LgsAAABwCXYP8JLUvn17bdiwQVWrVrWYTmPt7LthGLrrrru0cePGXBfCAgAAAMitWAK8JHXs2FFHjhzR3/72N/n4+Ng8A+/j46PRo0fr6NGjCg0NLa5yAAAAAJdg11Vobufv769Zs2bpvffe0y+//KKjR48qLi5OklStWjU1btxYbdu2lZubW3GWAQAAALiMYg3w2dzc3PTAAw/ogQceKInDuZTvD8U4uoRSIbxJPUeXAAAAUCoU2xQaAAAAAPZHgAcAAACcCAEeAAAAcCIEeAAAAMCJEOABAAAAJ+LSAf7nn39W48aNZTKZFBMT4+hyAAAAgDvmkgH+5s2bGjNmjDp16qSoqKgC77dnzx499thjqlatmry8vNSkSRO9/fbbSk1NLcZqAQAAgIJzuQB/8uRJNW/eXF999ZXWrVtX4P2WL1+u0NBQ/fHHH1q9erUOHjyooUOHavLkyerYsaOSk5OLsWoAAACgYErkRk4l6dChQwoLC9O7774rb2/vAu1z8uRJDR06VHfddZfWrVunKlWqSJLGjBmj1NRUvfnmmxo9erQ++uijYqwcAAAAyJ/LnYEPDw/XvHnzChzeJWnq1KlKTU3VkCFDzOE928iRI+Xp6alFixbpzJkzdq4WAAAAKByXC/Bubm6F6p+ZmanVq1dLksLCwizafX191aZNG2VlZWnVqlV2qREAAAAoKpcL8IUVFRWlhIQESVJISIjVPtnb9+7dW1JlAQAAAFaV+QB/8uRJ8/MaNWpY7RMYGGjRFwAAAHAEl7uItbCSkpIk3Zp64+7ubrVPxYoVJUmJiYl5jpWampprycnssQEAAAB7KfMB3p6mTZumyZMnO7oMAHB53x+KcXQJAOAwZX4KjY+Pj6RbF7Omp6db7XPjxg1Jty5ozcsbb7yhxMRE8+Ps2bP2LRYAAABlXpk/A1+/fn3z89jYWNWpU8eiz8WLFy36WuPp6SlPT0/7FggAAADkUObPwDdq1Mh8Zv3o0aNW+2Rvb9OmTYnVBQAAAFhT5gO8m5ub+vXrJ0natGmTRXtiYqL27t2rcuXK6Yknnijp8gAAAIBcynyAl6Rx48bJw8NDixcvtlhpZu7cuUpNTdXgwYMVFBTkoAoBAACAW1wywF++fFmxsbGKjY3Nc1u2Bg0a6OOPP1ZcXJx69uypnTt36tSpU3r//fc1ceJEtWrVSrNnzy7BdwAAAABY55IXsbZp00anT5/Ota1t27bm54ZhWOzz7LPPqmHDhpo2bZr69u2r5ORkBQcHa8KECRo7dqwqVKhQ7HUDAAA4o/kdOji6hFLhhZ9/LpHjuGSAj4mJKdJ+7du319q1a+1bDAAAAGBHLjmFBgAAAHBVBHgAAADAiRDgAQAAACdCgAcAAACcCAEeAAAAcCIEeAAAAMCJEOABAAAAJ0KABwAAAJwIAR4AAABwIgR4AAAAwIkQ4AEAAAAnQoAHAAAAnAgBHgAAAHAiBHgAAADAiRDgAQAAACdCgAcAAACcCAEeAAAAcCIEeAAAAMCJEOABAAAAJ0KABwAAAJxIeUcXAAAA4My+PxTj6BJQxnAGHgAAAHAiBHgAAADAiRDgAQAAACdCgAcAAACcCAEeAAAAcCIEeAAAAMCJEOABAAAAJ0KABwAAAJwIAR4AAABwIgR4AAAAwIkQ4AEAAAAnQoAHAAAAnAgBHgAAAHAiBHgAAADAiRDgAQAAACdCgAcAAACcCAEeAAAAcCIEeAAAAMCJEOABAAAAJ0KABwAAAJwIAR4AAABwIgR4AAAAwIkQ4AEAAAAnQoAHAAAAnAgBHgAAAHAiBHgAAADAiRDgAQAAACdCgAcAAACcCAEeAAAAcCIEeAAAAMCJEOABAAAAJ0KABwAAAJwIAR4AAABwIgR4AAAAwIkQ4AEAAAAnUt7RBZQ269ev1/vvv69ff/1Vqampaty4sYYOHaoXX3xR5crx+46jzO/QwdElONwLP//s6BIAAEApQCLNYcaMGerZs6cqVKigjRs36rffflP37t01atQoPfbYY8rIyHB0iQAAACjjOAP/f7Zv366///3vuvfee7V69Wq5u7tLuhXqr169qo8++kgzZszQm2++6eBKAQAAUJZxBv7/TJo0SZI0atQoc3jPNmbMGEnSu+++q5s3b5Z0aQAAAIAZAV7S5cuXFRERIUkKCwuzaG/cuLFq166tpKQkrVu3roSrAwAAAP6HAC9p3759ysrKkru7u+rXr2+1T0hIiCRp7969JVkaAAAAkAsBXtLJkyclSdWqVbO50kxgYGCuvgAAAIAjcBGrpKSkJEmSl5eXzT4VK1aUJCUmJtrsk5qaqtTUVPPr7L7Z4xfFjeRrRd7XldxkBaA7+h7ZC99HvovZHP195Lt4C99Hx38XJb6PEt/FbPb4PlauXFkmkynPPgR4O5o2bZomT55ssb1OnToOqAauZoyvr6NLAMz4PqK04LuI0sQe38fExET5+Pjk2YcAL5k/pLxWmLlx44YkyTePf5g33nhDr7zyivl1VlaW4uPjddddd+X7mxRsS0pKUp06dXT27Nl8v9BAceK7iNKE7yNKC76L9lW5cuV8+xDgJfOFq5cvX1ZWVpbVefAXL17M1dcaT09PeXp65tpWpUoV+xVaxvn4+PA/BpQKfBdRmvB9RGnBd7HkcBGrpFatWqlcuXJKT0+3eZHq0aNHJUlt2rQpydIAAACAXAjwkgICAtS5c2dJ0qZNmyzajx07pnPnzqly5cp65JFHSro8AAAAwIwA/38mTpwoSZozZ44ybruS+v3335ckvfbaa+bVaFByPD09NXHiRIvpSUBJ47uI0oTvI0oLvoslz2QYhuHoIkqLqVOn6s0331Tv3r01efJkeXt7a9GiRZoxY4Z69eqltWvXqnx5LhsAAACA4xDgb7Nu3Tq9//772rdvn9LT09WoUSMNGTJEI0eOlJubm6PLAwAAQBlHgAcAAACcCHPgUar9/PPPaty4sUwmk2JiYhxdDgAAsGH06NEymUyqV6+eo0txeQR4lEo3b97UmDFj1KlTJ0VFRTm6HJRhZ86c0VtvvaXQ0FD5+fnJ3d1d1atXV69evbRq1SpHl4cyJDY2Vp988on69eunoKAgeXp6ysvLS40aNdJf/vIX/l8Jh9qzZ48++OADR5dRZhDgUeqcPHlSzZs311dffaV169Y5uhyUYVu3blX9+vX1zjvvqGvXrtq4caOOHDmiuXPn6sSJE3rqqac0YMAAZWVlObpUlAG9e/fW8OHD5ePjo+XLlysqKkrbt29X7969tXDhQrVo0UI7duxwdJkog9LS0jRs2DDVrl3b0aWUGSypglLn0KFDCgsL07vvvitvb29Hl4My7OrVq8rIyNCsWbM0atQo8/YGDRqoQ4cOCgkJ0YoVK9SlSxeNGDHCgZWirAgPD9eSJUvMr4OCgtS6dWvFx8dr6dKlmjRpkn766SfHFYgyaerUqTp//rzmzZunAQMGOLqcMoEz8Ch1wsPDNW/ePMI7SgU3Nzc999xzFttr1qypPn36SJJWrlxZ0mWhDJo6dapmzpxpta1Vq1aSpEuXLpVkSYAOHTqkadOmaebMmQoMDHR0OWUGAR6lDst1orQIDw9XfHy8fHx8rLZn/7k4Pj6+JMtCGdWjRw+FhIRYbdu9e7ckqXv37iVZEsq4rKwsDRs2TA8++KCGDRvm6HLKFKbQAIAN7u7ucnd3t9l+8eJFSdJ9991XUiUBZunp6YqOjtb8+fO1fPly9evXT2+//bajy0IZ8q9//Uu///67Dhw44OhSyhwCPAAUQUZGhjZu3ChJGjlypIOrQVnTv39/ffnllzIMQ/Xr19eaNWvUt29fR5eFMiQ6Olrjx4/XlClTVL9+fUeXU+YwhQYAimDRokWKjY3VCy+8oDZt2ji6HJQx//znP3X48GH9+OOP6tChgx577DH96U9/UkJCgqNLQxkxYsQI3XPPPRo9erSjSymTOAMPAIV04sQJvfrqq3rggQc0a9YsR5eDMqhGjRqqUaOGQkJC1KNHD1WrVk2zZs1SdHS0du/ezbVEKFaffPKJtm7dqn379vFdcxDOwANAIVy8eFE9evRQgwYNtG7dOlWoUMHRJQGaNGmSPDw8tG/fPn377beOLgcu7OLFi3r11Vc1duxYNWvWzNHllFkEeAAooAsXLigsLEwBAQHatGmTfH19HV0SIEmqXLmy+fb1u3btcmwxcGkbNmxQQkKC/vnPf8rb2zvXo2fPnpJu3cE653bYH1NoAKAATp8+rbCwMNWuXVvfffcdP5RQoi5cuKBvvvlGI0aMsDlloVKlSpJu3RUTKC79+vVTaGio1bY9e/Zo0KBBqlmzpiIiIkq2sDKGAA8A+Th+/LjCwsJ0zz33aM2aNfLy8jK3/f777xo7dqzWr1/vwArh6qKiovTCCy+oa9euatSokUV7enq6oqKiJN26UzBQXCpXrqzKlStbbTt37pwkqXz58nwPixlTaAAgD4cOHVKnTp3UsmVLffPNN7nCu3TrJk4//vijg6pDWbNgwQKr22fOnKnr16+rUqVKeuKJJ0q4KgAljTPwKJUuX76szMxMi23ZFwzWqFHDEWWhjDl48KC6dOmiuLg4xcTEWP2z8bVr1xxQGcqa8uVv/biePXu24uPjNWTIENWtW1fnz5/X8uXLtWDBAnl6emrJkiX8/xElLvtndvZdqTMzMxUbGytJzIMvJibDMAxHFwHcrl69ejp9+rTNdr62KAmzZ88u8BrHfCdR3CIjI7VixQpt27ZNx48fV1JSkjw9PRUcHKyuXbtq1KhRatiwoaPLRBmU18/siRMnatKkSSVbUBlAgAcAAACcCHPgAQAAACdCgAcAAACcCAEeAAAAcCIEeAAAAMCJEOABAAAAJ0KABwAAAJwIAR4AAABwIgR4AAAAwIkQ4AEAAAAnQoAHAAAAnAgBHgAAAHAiBHgAgJnJZMrzUa9ePUeXCABlXnlHFwAAKD08PT0lSZmZmcrIyHBwNQAAazgDDwAwS0lJUUpKij766CNHlwIAsIEADwAOsGTJEqaoAACKhAAPAAAAOBECPAAAAOBECPAAAACAEyHAAwAAAE6EZSQBwEUcP35ckZGRunLlihISEuTl5SU/Pz81bNhQrVu3Ni8RWVplZmZq//79OnTokK5cuaIbN27Iz89PgYGB6tChgwICAux+zDNnzmjHjh36448/lJaWpurVqys0NFQNGzYs9FjR0dE6dOiQTp8+revXrystLU1eXl7y9/dX3bp11ahRI9WqVcvu7wFA2UOAB4ASEhMTo+Dg4Dz7nD59WiaTyWK7YRhW+58/f15z5szR4sWL9ccff9gc18PDQw8//LBGjx6tLl26FK7wAnjooYe0detWm+2dO3dWRESE1baTJ0/q/fff12effaZr165Z7WMymdSmTRuNGzdOffv2tXkca59dTkFBQYqJidGJEyf0/PPPa8uWLVb7de/eXR999JGCgoLyHC8lJUVz5szRwoULdfz48Tz7SpKfn59at26tBx54QJMmTcq3XgCwxmTY+qkAALCr06dPq3HjxpLyvlGStTPlKSkpFtsWLVqkv/71r7p+/bp5W7ly5dSnTx81b95ccXFx+s9//qNLly7l2q9///6aP3++fH19bda6ZMkSDRkyxGJ7dgC+XY8ePbRt2zZlZGQoMzNTkuTu7q5y5W7N1OzUqZM2bNhgsd/cuXM1ZswYpaammrdVrFhRgwYNUvXq1fXDDz/o119/zbXPk08+qU8//VReXl4W41WoUEGS7c83KChIK1euVFhYmJKTk22+f0kKDAxUZGSkatSoYbU9Li5OjzzyiEV9VapUUXh4uOrUqaOKFSsqNjZWEREROnz4cK5+6enpKl+e82gAisAAAJS4xYsXG5IsHkFBQQXaf/LkyVb3X7FiRa5+ly5dMoKDgy36NW/e3EhKSrJrfbt37zYCAgIMScaf/vQnIyUlJc/3MGXKFIvxTSaTsWXLFnOfjIwMo0ePHhb9evToYWRlZRW6fj8/P6N69epGQECA8dprrxkffvihMWrUKKNChQpW+z/zzDM2j/Hkk09a9O/YsaNx7do1q/0///xzw9PT09w3PT09z88HAGwhwAOAA9xJgF+1apXVfTt27Gi1/6JFi6z2f/TRR+1W31dffWV4eXkZkowxY8bkGa4NwzDWr19vmEwmi/G7dOli0Xfr1q1Wa3nvvfcKXb8ko27dukZsbGyu/uvWrbPat3z58sbly5ctxj937pzV+letWpXn+3777bcJ8ADuGKvQAIATSUtL02uvvWa1rWfPnla3P/LII1a3f/PNN9q8efMd1zRr1iw99dRTSk1N1QcffKCZM2fmObfbMAy9/vrrVuf1d+7c2WJb+/bt5e7ubrF9xowZVqcW5WfChAmqXr16rm0PP/yw7rrrLou+GRkZ2rVrl8X23377zWr9sbGxeR77ueeeK2S1AGCJAA8ATmTdunWKjo622ta0aVOr2wMDA1W1alWrbXPmzClyLZmZmXrppZc0ZswYeXp66quvvtKoUaPy3W/v3r3av3+/1bYGDRpYbPPw8LC6ektcXJy++uqrQtfdq1cvi20mk8nmBcbWPu+kpCSrfcePH6/58+fr6tWrVttr166to0eP6siRI8x/B1BkBHgAcCLr16+32RYYGGizzdaFmJs2bbJ5MW1erl+/rscee0xz586VJLVs2VJ9+vQp0L4bN2602ebv71+o7XmtfGONt7e3atasabWtYsWKVrcnJiZabKtdu7bVvgkJCXrxxRcVEBCg9u3ba+zYsfrhhx9048YNc5/GjRsrJCSkUHUDQE4EeABwIrevZJJTXqvK2GpLSkrS+fPnC1XDxYsX1blzZ3333XfmbTt37tTf//73Au1/9OhRm23Zq8jcztYa9nv37i3QMbPl9RnZmvaTvapOTu3bt8/zF6aMjAzt2bNH7733nsLDw1W1alX16dNHX3/9tc0lQQGgoAjwAOBELl++bLPNw8PDZlteN3G6fZnJvCQmJqp9+/YWSydK0syZM/Xll1/mO8aVK1dstvXo0UMVKlSwePz8889W++e19r019pq24uHhofnz58vNza1A/VNTU/Xdd9+pX79+atGihQ4ePGiXOgCUTQR4AECBJSQk6MyZM2rVqpXV9mHDhunAgQNFHj8tLU2pqakWD1tnrW3NNS8Jffv21aZNm2xee2DL/v371blzZ6vr6QNAQRDgAcCJVKtWzWZbWlqazbacN0q6XUBAQKFqGD9+vPbu3avhw4dbtGXPjc8rWFtb7SVbRESEjFtLHBfocfPmzULVbm+dO3fW77//rk2bNunFF19Uo0aNCrRffHy83nrrrWKuDoCrIsADgBNp0qSJzTZrF1vm1+bj42N1hRdbgoKC9NZbb8lkMmnu3Ll64IEHLPqcOnVK/fv3V1ZWltUxrK00ky2/u6OWRiaTSV27dtXcuXN17NgxXbx4UV9++aWGDx8uPz8/m/tZuzMtABQEAR4AnIitNd2lWxeX2mJrffJu3boVeV64h4eHvvrqK6uruvz444968803re7XpUsXm2MWZFpJdHS0lixZoiVLluiXX34pcL32FB0drb/97W+aOXOmRVuNGjX0pz/9SR999JFiYmJsvt/CXHsAADkR4AHAAWxdVHr7WeuEhATNnj1bs2fP1tGjR9WzZ0/dfffdVve1dWHkxYsXFR8fb7XtpZdeKkTVlgIDA7V69Wqr72f69OlW12kPDQ21eRbe2k2Tbvfee+9pyJAhGjJkiE6fPl34ou3g/Pnz+te//qWJEyfq+vXrNvv5+PjYnCpja21+AMgPAR4AHMDWuubXrl3L9frIkSMaPXq0Ro8eraioKLm7u+u9996zuu+6deusbre1dvxjjz2W59nwgmrXrp3mz59vtW3w4ME6dOhQrm1ubm6aNGmS1f5r167N88z0mTNn9MUXX0i6NZ3n0UcfLVrRdnLjxg198MEHefaxtb58hw4diqMkAGUAAR4AHKBly5ZWlyBMSEjQhQsXzK9znsFu3ry5JKlfv36aPHmyxb7bt283h9tsly5dsnoGuGXLllq2bFlRy7cwZMgQq3dhTU5O1uOPP24xB3/gwIF6/vnnrfZ/5plnrF4Ee+TIEfXu3VtXr15VuXLlNG/evDyXxywpEyZM0Jw5c6yulJOSkmL18y9XrpxeeeWVkigPgAsyGdxRAgAc4tlnn9Vnn31msb1p06Z67LHHFBUVpZUrV0qSevfurW+//TZXv08++UQvv/xyrikc5cqVU58+fdSiRQvFxcVp5cqVFme0BwwYoPnz58vHx8fi2Nk3UsrMzLR5h9bs0LxhwwZ16tRJy5Yt04gRI2QYhs2VcNzd3VWuXDl99NFHevbZZ83HmDBhgqZPn24Rfv38/NSnTx/dfffdSkxM1JEjR7RhwwZlZWWpQoUKWrhwoXmcO60/e5+0tDSrIdzNzc18nUD2Pjt27FDHjh1z9QsKClL37t1Vp04dpaam6uzZs/r+++8tpi+VL19ec+fOtfoLDAAUiAEAcIhr164ZAwcONNzc3AxJVh8mk8no27evER8fb3WMs2fPGq+//roREBBgcwxJhoeHh/Hoo48amzdvzrOmvMa4/bFlyxbDMAxj8eLFBd5n8eLFFsfct2+f8fTTTxsVKlTIc18fHx9j8ODBRlRUlF3rL8o+Z8+eNYYOHWo0adIkz3+/nI8qVaoYgwYNMg4cOJDvdwMA8sIZeABwsPj4eO3evVvR0dFKSkpSZmamKlWqpLp166p9+/YFXubx2LFj+u233xQXF6fExER5eXnJz89PDRs2VOvWrc1nmkurtLQ07du3T8eOHdPVq1d148YNeXl5qVq1agoJCVGLFi3k7u7u6DItpKam6tixY4qJidHFixeVmJio1NRUubu7q1KlSgoMDFTDhg3VtGnTAt+5FQDyQoAHAAAAnAgXsQIAAABOhAAPAAAAOBECPAAAAOBECPAAAACAEyHAAwAAAE6EAA8AAAA4EQI8AAAA4EQI8AAAAIATIcADAAAAToQADwAAADgRAjwAAADgRAjwAAAAgBMhwAMAAABOhAAPAAAAOJH/D7zrxfhAu75IAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 810x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphs_3_and_4(he_male_num_of_tokens_map, he_female_num_of_tokens_map, he_max_tokens, \n",
    "               \"Hebrew num of tokens per Gender\", \"Male\", \"Female\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f807985f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 810x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphs_3_and_4(he_stereotype_num_of_tokens_map, he_anti_stereotype_num_of_tokens_map, he_max_tokens, \"Hebrew num of tokens per stereotype\", \"stereotype\", \"Anti stereotype\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2b6ae468",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 810x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphs_3_and_4(de_stereotype_num_of_tokens_map, de_anti_stereotype_num_of_tokens_map, de_max_tokens, \"German num of tokens per stereotype\", \"stereotype\", \"Anti stereotype\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
